Releases

AWS introduced pretrained models and solution templates to accelerate machine-learning deployment.

NVIDIA introduced a deskside AI system with four A100 GPUs for workgroups.

NVIDIA doubled A100 accelerator memory to 80GB using HBM2e.

Baidu opened Apollo Go autonomous ride-hailing to public users in Beijing.

NVIDIA introduced Maxine, a cloud-native suite of GPU-accelerated AI video-conferencing and streaming software.

Scale released the core Nucleus workspace for dataset visualization, search, curation, collaboration, and model-error analysis.

Scale launched Nucleus as a dataset-management and model-debugging workspace for visualizing, curating, searching, and collaborating on machine-learning data.

Scale introduced a self-serve, pay-as-you-go product for receiving production-quality labels and instruction feedback in as little as one hour.

Scale launched a mapping product for computer-vision and autonomous path-planning workflows.

Hugging Face began its hosted inference platform with a serverless API for models on the Hub, later expanding into dedicated endpoints and provider routing.

Hugging Face launched hosted dataset repositories alongside its Datasets library, creating a shared catalog for reusable machine-learning data.

NVIDIA began shipping DGX A100, an integrated 5-petaflop AI system with eight A100 GPUs.

NVIDIA launched the A100 Tensor Core GPU, its first Ampere data-center accelerator for training and inference.

Hugging Face made model repositories a central Hub product for versioning, documenting, discovering, and reusing pretrained models.

Hugging Face expanded from an NLP library into a Git-based collaboration platform for sharing models and machine-learning artifacts.

Oracle launched a managed collaborative environment for building, training and deploying machine-learning models.

CoreWeave established a managed, multi-tenant Kubernetes cloud running directly on bare metal for GPU-accelerated machine-learning and inference workloads.

AWS launched automated model building with full visibility into generated models and notebooks.

AWS launched a web-based integrated development environment for machine learning.

AWS introduced managed human-review workflows for machine-learning predictions.

AWS introduced an intelligent enterprise-search service using machine learning and natural-language queries.

AWS introduced ML-powered code review and performance recommendations.

AWS made Inferentia-powered EC2 instances generally available for machine-learning inference.

AWS introduced a generative-AI learning service and musical keyboard.

CoreWeave launched its specialized cloud business around on-demand NVIDIA GPU infrastructure for compute-intensive workloads.

NVIDIA expanded EGX with certified servers, software and ecosystem support for AI, IoT and 5G at the edge.

Databricks introduced the MLflow Model Registry for centralized model versioning and lifecycle management.

NVIDIA introduced EGX to bring real-time AI computing to edge and industrial environments.

MLflow 1.0 marked the first stable major release of the open-source ML lifecycle platform.

Scale introduced a managed workspace for configuring annotation projects, instructions, quality controls, and review workflows.

AWS launched attachable fractional GPU acceleration for EC2 and SageMaker inference workloads.

AWS launched a reinforcement-learning education platform built around an autonomous scale race car.

AWS introduced document AI for extracting text, handwriting, forms and tables.

AWS introduced a managed forecasting service based on technology used in Amazon's retail operations.

AWS introduced a managed service for building individualized recommendations using technology derived from Amazon.com.

AWS introduced a managed data-labeling service combining human annotation with active learning.

AWS launched a HIPAA-eligible NLP service for extracting medical entities and relationships from text.

Oracle launched a multi-skill enterprise digital assistant using natural-language understanding and machine learning.

NVIDIA launched the T4 GPU and TensorRT 5 as a data-center inference platform.

Scale expanded its annotation platform around the image, video, lidar, and sensor-fusion requirements of autonomous-vehicle developers.

Baidu announced its first Kunlun AI accelerator for cloud and edge workloads.

Baidu introduced its in-house Kunlun AI computing architecture.

Baidu launched a full-featured machine-learning platform for algorithm developers.

Apollo 3.0 added production support for autonomous minibuses, delivery vehicles and valet parking.

Baidu launched a PaddlePaddle development and education platform for model building.

Baidu Brain 3.0 added multimodal semantic understanding and more than 110 AI capabilities.

Databricks introduced MLflow as an open-source platform for managing the machine-learning lifecycle.

NVIDIA introduced the Jetson AGX Xavier embedded AI computer for autonomous machines.

Apollo 2.5 added high-speed-road support, lower costs, developer improvements and ApolloScape datasets.

Oracle released the first Autonomous Database cloud service, powered by machine-learning automation.

NVIDIA introduced Clara as a medical imaging and healthcare AI computing platform.

NVIDIA introduced DGX-2 with 16 V100 GPUs connected by NVSwitch for large-scale AI training.

Apollo 2.0 added cloud services, reference hardware and vehicle platforms for simple urban roads.

NVIDIA launched TITAN V, bringing Volta Tensor Core AI acceleration to desktop systems.

AWS introduced managed natural-language processing for entity, sentiment, language and topic extraction.

AWS introduced a managed neural machine-translation service.

AWS introduced a managed automatic speech-recognition service.

AWS introduced a programmable deep-learning-enabled video camera for edge computer-vision development.

AWS launched an end-to-end managed platform for building, training and deploying machine-learning models.

Baidu upgraded DuerOS with expanded conversational AI capabilities.

Baidu launched a no-code interface for training and deploying custom AI models.

Oracle unveiled a self-driving database cloud using machine learning for tuning, patching, security and operations.

Microsoft launches Seeing AI, an accessibility application that narrates people, text, products and surroundings for blind and low-vision users.

Baidu introduced a production-oriented intelligent-driving solution for automakers.

Google announces its second-generation TPU and plans to offer Cloud TPUs to Google Cloud customers.

NVIDIA introduced its GPU Cloud catalog, later branded NGC, for optimized deep-learning containers and software.

NVIDIA introduced the Volta architecture and V100 GPU, including Tensor Cores built for deep learning.

Baidu announced its open autonomous-driving platform.

Baidu introduced its voice-assistant experience powered by DuerOS.

Baidu introduced its conversational AI operating system for devices and services.

IBM commercialized enterprise conversational-AI tooling that evolved into watsonx Assistant.

IBM launched an AI-powered enterprise search and document-understanding service.

Scale began building distributed contributor platforms and training operations to support large-scale annotation, expert-data, and evaluation programs.

Scale launched Remotasks to train and coordinate distributed contributors performing data annotation and review tasks.

Amazon unveiled a checkout-free store using computer vision, sensor fusion and deep learning.

OpenAI released Universe, a software platform for training intelligent agents across games, websites, and applications.

AWS introduced a managed text-to-speech service with natural-sounding voices.

AWS introduced the conversational AI service built from the speech recognition and language understanding technology behind Alexa.

AWS introduced managed image analysis for object, scene and face detection.

Oracle introduced the infrastructure platform that became OCI, later forming the base for its managed AI services and GPU cloud.

Baidu launched its core AI technology engine and open-capability platform.

Scale launched the data infrastructure that became its core Data Engine, combining APIs, machine assistance, and human review to create high-quality training data.

Google publicly reveals the TPU, a custom accelerator designed to speed machine-learning workloads in its data centers.

Facebook introduced FBLearner Flow, its internal platform for managing the full lifecycle of machine-learning models.

OpenAI released the public beta of Gym, a toolkit for developing and comparing reinforcement-learning algorithms.

The Pascal-based Tesla P100 accelerator debuted alongside DGX-1 for deep learning and HPC.

NVIDIA unveiled DGX-1, its first integrated deep-learning supercomputer.

Microsoft launches Cognitive Services APIs for adding vision, speech, language, knowledge and search capabilities to applications.

Baidu began building enterprise cloud services that evolved into Baidu AI Cloud.

IBM created Watson Health to apply data and cognitive-computing technologies across healthcare and life sciences.

AWS launched its first managed machine-learning service for building predictive models from data in S3, Redshift and RDS.

Microsoft makes Azure Machine Learning generally available as a cloud platform for creating and deploying predictive models.

Amazon introduced the Alexa voice service with the first Echo smart speaker.

Microsoft launches Xiaoice, a social conversational AI designed for long-term, emotionally engaging interactions.

Microsoft introduces Cortana as a personal digital assistant for Windows Phone, later expanding it across Windows and Microsoft 365.

Amazon launched a marketplace for human intelligence tasks that became widely used for labeling and evaluating machine-learning data.